class_docstr: Softplus function that ensures a strictly positive activation
  
  Parameters
  ----------
  margin : float
      the minimum value of activation. when =0, same as vanilla softplus
  beta: float
  
  threshold: float
class_docstr_tok: ["Softplus","function","that","ensures","a","strictly","positive","activation","Parameters","--","--","--","--","--","margin",":","float","the","minimum","value","of","activation",".","when","=0",",","same","as","vanilla","softplus","beta",":","float","threshold",":","float"]
code: def __init__(
      self,
      margin: float = 0.0,
      beta: float = 1.0,
      threshold: float = 20.0,
  ):
      super(PositiveSoftplus, self).__init__(beta, threshold)
      assert margin >= 0.0
      self.margin = margin
  
file_path: gluonts/src/gluonts/nursery/daf/tslib/nn/activations.py
imports: from typing import List
  import math
  import torch
  from torch import Tensor, nn
label_desc: Use image node_id 1 to create a new PositiveSoftplus object from inherited base classes: nn with example: obj = PositiveSoftplus(margin, beta, threshold)